Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations136145
Missing cells2202017
Missing cells (%)44.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.4 MiB
Average record size in memory288.0 B

Variable types

Numeric16
Text12
Categorical7
DateTime1

Alerts

depth_chart_position is highly overall correlated with ngs_position and 1 other fieldsHigh correlation
entry_year is highly overall correlated with espn_id and 8 other fieldsHigh correlation
espn_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
fantasy_data_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
game_type is highly overall correlated with weekHigh correlation
gsis_it_id is highly overall correlated with entry_year and 9 other fieldsHigh correlation
height is highly overall correlated with weightHigh correlation
jersey_number is highly overall correlated with ngs_position and 2 other fieldsHigh correlation
ngs_position is highly overall correlated with depth_chart_position and 4 other fieldsHigh correlation
pff_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
position is highly overall correlated with depth_chart_position and 2 other fieldsHigh correlation
rookie_year is highly overall correlated with entry_year and 8 other fieldsHigh correlation
rotowire_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
season is highly overall correlated with entry_year and 10 other fieldsHigh correlation
sleeper_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
status is highly overall correlated with ngs_position and 1 other fieldsHigh correlation
status_description_abbr is highly overall correlated with statusHigh correlation
week is highly overall correlated with game_typeHigh correlation
weight is highly overall correlated with heightHigh correlation
yahoo_id is highly overall correlated with entry_year and 8 other fieldsHigh correlation
years_exp is highly overall correlated with gsis_it_idHigh correlation
status is highly imbalanced (76.1%)Imbalance
status_description_abbr is highly imbalanced (76.9%)Imbalance
position has 1510 (1.1%) missing valuesMissing
depth_chart_position has 30854 (22.7%) missing valuesMissing
birth_date has 2861 (2.1%) missing valuesMissing
college has 100526 (73.8%) missing valuesMissing
gsis_id has 53093 (39.0%) missing valuesMissing
espn_id has 113048 (83.0%) missing valuesMissing
sportradar_id has 112023 (82.3%) missing valuesMissing
yahoo_id has 113891 (83.7%) missing valuesMissing
rotowire_id has 111909 (82.2%) missing valuesMissing
pff_id has 113745 (83.5%) missing valuesMissing
pfr_id has 118709 (87.2%) missing valuesMissing
fantasy_data_id has 115259 (84.7%) missing valuesMissing
sleeper_id has 112839 (82.9%) missing valuesMissing
years_exp has 79743 (58.6%) missing valuesMissing
headshot_url has 2124 (1.6%) missing valuesMissing
esb_id has 53258 (39.1%) missing valuesMissing
gsis_it_id has 100174 (73.6%) missing valuesMissing
smart_id has 53283 (39.1%) missing valuesMissing
entry_year has 79743 (58.6%) missing valuesMissing
rookie_year has 79755 (58.6%) missing valuesMissing
draft_club has 100288 (73.7%) missing valuesMissing
ngs_position has 126663 (93.0%) missing valuesMissing
week has 79239 (58.2%) missing valuesMissing
game_type has 79239 (58.2%) missing valuesMissing
status_description_abbr has 85392 (62.7%) missing valuesMissing
football_name has 79259 (58.2%) missing valuesMissing
draft_number has 102533 (75.3%) missing valuesMissing
yahoo_id is highly skewed (γ1 = 28.15370803)Skewed
jersey_number has 65918 (48.4%) zerosZeros
years_exp has 10884 (8.0%) zerosZeros

Reproduction

Analysis started2024-08-09 02:32:17.938423
Analysis finished2024-08-09 02:32:50.095695
Duration32.16 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

season
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.1024
Minimum1920
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:50.169763image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1939
Q11975
median1996
Q32013
95-th percentile2022
Maximum2024
Range104
Interquartile range (IQR)38

Descriptive statistics

Standard deviation25.49723
Coefficient of variation (CV)0.012805585
Kurtosis-0.020367663
Mean1991.1024
Median Absolute Deviation (MAD)18
Skewness-0.80861948
Sum2.7107864 × 108
Variance650.10876
MonotonicityIncreasing
2024-08-08T20:32:50.297886image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018 3142
 
2.3%
2022 3134
 
2.3%
2019 3114
 
2.3%
2023 3090
 
2.3%
2017 3082
 
2.3%
2020 3068
 
2.3%
2016 3061
 
2.2%
2021 2961
 
2.2%
2024 2909
 
2.1%
1987 2817
 
2.1%
Other values (95) 105767
77.7%
ValueCountFrequency (%)
1920 369
0.3%
1921 467
0.3%
1922 412
0.3%
1923 427
0.3%
1924 413
0.3%
1925 495
0.4%
1926 551
0.4%
1927 318
0.2%
1928 231
0.2%
1929 293
0.2%
ValueCountFrequency (%)
2024 2909
2.1%
2023 3090
2.3%
2022 3134
2.3%
2021 2961
2.2%
2020 3068
2.3%
2019 3114
2.3%
2018 3142
2.3%
2017 3082
2.3%
2016 3061
2.2%
2015 2190
1.6%

team
Text

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:50.491172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7576775
Min length2

Characters and Unicode

Total characters375444
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAKR
2nd rowAKR
3rd rowAKR
4th rowAKR
5th rowAKR
ValueCountFrequency (%)
gb 5378
 
4.0%
det 5185
 
3.8%
was 5054
 
3.7%
phi 4966
 
3.6%
pit 4853
 
3.6%
sf 4567
 
3.4%
buf 4430
 
3.3%
nyg 4388
 
3.2%
min 4050
 
3.0%
kc 4046
 
3.0%
Other values (76) 89228
65.5%
2024-08-08T20:32:50.777432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 38141
 
10.2%
N 34416
 
9.2%
I 31134
 
8.3%
C 24403
 
6.5%
T 23594
 
6.3%
L 23113
 
6.2%
E 22534
 
6.0%
S 21142
 
5.6%
D 20004
 
5.3%
B 19620
 
5.2%
Other values (16) 117343
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 38141
 
10.2%
N 34416
 
9.2%
I 31134
 
8.3%
C 24403
 
6.5%
T 23594
 
6.3%
L 23113
 
6.2%
E 22534
 
6.0%
S 21142
 
5.6%
D 20004
 
5.3%
B 19620
 
5.2%
Other values (16) 117343
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 38141
 
10.2%
N 34416
 
9.2%
I 31134
 
8.3%
C 24403
 
6.5%
T 23594
 
6.3%
L 23113
 
6.2%
E 22534
 
6.0%
S 21142
 
5.6%
D 20004
 
5.3%
B 19620
 
5.2%
Other values (16) 117343
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 38141
 
10.2%
N 34416
 
9.2%
I 31134
 
8.3%
C 24403
 
6.5%
T 23594
 
6.3%
L 23113
 
6.2%
E 22534
 
6.0%
S 21142
 
5.6%
D 20004
 
5.3%
B 19620
 
5.2%
Other values (16) 117343
31.3%

position
Categorical

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)< 0.1%
Missing1510
Missing (%)1.1%
Memory size1.0 MiB
OL
18563 
DL
16889 
DB
16744 
RB
16294 
WR
14504 
Other values (23)
51641 

Length

Max length4
Median length2
Mean length1.9924537
Min length1

Characters and Unicode

Total characters268254
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDL
2nd rowDL
3rd rowDL
4th rowDL
5th rowDL

Common Values

ValueCountFrequency (%)
OL 18563
13.6%
DL 16889
12.4%
DB 16744
12.3%
RB 16294
12.0%
WR 14504
10.7%
LB 13226
9.7%
QB 7361
 
5.4%
TE 7096
 
5.2%
CB 2752
 
2.0%
DE 2277
 
1.7%
Other values (18) 18929
13.9%

Length

2024-08-08T20:32:50.888583image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ol 18563
13.8%
dl 16889
12.5%
db 16744
12.4%
rb 16294
12.1%
wr 14504
10.8%
lb 13226
9.8%
qb 7361
 
5.5%
te 7096
 
5.3%
cb 2752
 
2.0%
de 2277
 
1.7%
Other values (18) 18929
14.1%

Most occurring characters

ValueCountFrequency (%)
B 60767
22.7%
L 53123
19.8%
D 37504
14.0%
R 30810
11.5%
O 20744
 
7.7%
W 14504
 
5.4%
T 11222
 
4.2%
E 10936
 
4.1%
Q 7361
 
2.7%
S 5780
 
2.2%
Other values (8) 15503
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 60767
22.7%
L 53123
19.8%
D 37504
14.0%
R 30810
11.5%
O 20744
 
7.7%
W 14504
 
5.4%
T 11222
 
4.2%
E 10936
 
4.1%
Q 7361
 
2.7%
S 5780
 
2.2%
Other values (8) 15503
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 60767
22.7%
L 53123
19.8%
D 37504
14.0%
R 30810
11.5%
O 20744
 
7.7%
W 14504
 
5.4%
T 11222
 
4.2%
E 10936
 
4.1%
Q 7361
 
2.7%
S 5780
 
2.2%
Other values (8) 15503
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 60767
22.7%
L 53123
19.8%
D 37504
14.0%
R 30810
11.5%
O 20744
 
7.7%
W 14504
 
5.4%
T 11222
 
4.2%
E 10936
 
4.1%
Q 7361
 
2.7%
S 5780
 
2.2%
Other values (8) 15503
 
5.8%

depth_chart_position
Categorical

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)< 0.1%
Missing30854
Missing (%)22.7%
Memory size1.0 MiB
WR
11329 
RB
8279 
DE
7241 
DB
6843 
G
6593 
Other values (31)
65006 

Length

Max length3
Median length2
Mean length1.8953282
Min length1

Characters and Unicode

Total characters199561
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowDG
5th rowDG

Common Values

ValueCountFrequency (%)
WR 11329
 
8.3%
RB 8279
 
6.1%
DE 7241
 
5.3%
DB 6843
 
5.0%
G 6593
 
4.8%
LB 6224
 
4.6%
QB 5904
 
4.3%
DT 5651
 
4.2%
OT 5567
 
4.1%
CB 5360
 
3.9%
Other values (26) 36300
26.7%
(Missing) 30854
22.7%

Length

2024-08-08T20:32:51.002565image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wr 11329
 
10.8%
rb 8279
 
7.9%
de 7241
 
6.9%
db 6843
 
6.5%
g 6593
 
6.3%
lb 6224
 
5.9%
qb 5904
 
5.6%
dt 5651
 
5.4%
ot 5567
 
5.3%
cb 5360
 
5.1%
Other values (26) 36300
34.5%

Most occurring characters

ValueCountFrequency (%)
B 45325
22.7%
D 21382
10.7%
R 19609
9.8%
T 19476
9.8%
E 13811
 
6.9%
L 13292
 
6.7%
W 11476
 
5.8%
O 9719
 
4.9%
C 9688
 
4.9%
G 8546
 
4.3%
Other values (10) 27237
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 45325
22.7%
D 21382
10.7%
R 19609
9.8%
T 19476
9.8%
E 13811
 
6.9%
L 13292
 
6.7%
W 11476
 
5.8%
O 9719
 
4.9%
C 9688
 
4.9%
G 8546
 
4.3%
Other values (10) 27237
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 45325
22.7%
D 21382
10.7%
R 19609
9.8%
T 19476
9.8%
E 13811
 
6.9%
L 13292
 
6.7%
W 11476
 
5.8%
O 9719
 
4.9%
C 9688
 
4.9%
G 8546
 
4.3%
Other values (10) 27237
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 45325
22.7%
D 21382
10.7%
R 19609
9.8%
T 19476
9.8%
E 13811
 
6.9%
L 13292
 
6.7%
W 11476
 
5.8%
O 9719
 
4.9%
C 9688
 
4.9%
G 8546
 
4.3%
Other values (10) 27237
13.6%

jersey_number
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.1%
Missing444
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean27.105217
Minimum0
Maximum99
Zeros65918
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:51.120198image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q355
95-th percentile91
Maximum99
Range99
Interquartile range (IQR)55

Descriptive statistics

Standard deviation33.296363
Coefficient of variation (CV)1.2284116
Kurtosis-0.85446153
Mean27.105217
Median Absolute Deviation (MAD)4
Skewness0.81616019
Sum3678205
Variance1108.6478
MonotonicityNot monotonic
2024-08-08T20:32:51.245384image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65918
48.4%
23 893
 
0.7%
94 888
 
0.7%
21 887
 
0.7%
55 884
 
0.6%
26 876
 
0.6%
93 869
 
0.6%
91 867
 
0.6%
97 866
 
0.6%
25 865
 
0.6%
Other values (90) 61888
45.5%
ValueCountFrequency (%)
0 65918
48.4%
1 320
 
0.2%
2 498
 
0.4%
3 524
 
0.4%
4 591
 
0.4%
5 520
 
0.4%
6 486
 
0.4%
7 533
 
0.4%
8 523
 
0.4%
9 547
 
0.4%
ValueCountFrequency (%)
99 789
0.6%
98 859
0.6%
97 866
0.6%
96 850
0.6%
95 856
0.6%
94 888
0.7%
93 869
0.6%
92 800
0.6%
91 867
0.6%
90 832
0.6%

status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)< 0.1%
Missing27
Missing (%)< 0.1%
Memory size1.0 MiB
ACT
114097 
RES
 
8797
CUT
 
5736
DEV
 
3359
INA
 
1067
Other values (15)
 
3062

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters408354
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowACT
2nd rowACT
3rd rowACT
4th rowACT
5th rowACT

Common Values

ValueCountFrequency (%)
ACT 114097
83.8%
RES 8797
 
6.5%
CUT 5736
 
4.2%
DEV 3359
 
2.5%
INA 1067
 
0.8%
TRC 993
 
0.7%
TRT 834
 
0.6%
TRD 509
 
0.4%
RET 137
 
0.1%
UFA 124
 
0.1%
Other values (10) 465
 
0.3%

Length

2024-08-08T20:32:51.358491image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
act 114097
83.8%
res 8797
 
6.5%
cut 5736
 
4.2%
dev 3359
 
2.5%
ina 1067
 
0.8%
trc 993
 
0.7%
trt 834
 
0.6%
trd 509
 
0.4%
ret 137
 
0.1%
ufa 124
 
0.1%
Other values (10) 465
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 123259
30.2%
C 120826
29.6%
A 115291
28.2%
E 12348
 
3.0%
R 11412
 
2.8%
S 9080
 
2.2%
U 6057
 
1.5%
D 3868
 
0.9%
V 3359
 
0.8%
N 1253
 
0.3%
Other values (9) 1601
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 408354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 123259
30.2%
C 120826
29.6%
A 115291
28.2%
E 12348
 
3.0%
R 11412
 
2.8%
S 9080
 
2.2%
U 6057
 
1.5%
D 3868
 
0.9%
V 3359
 
0.8%
N 1253
 
0.3%
Other values (9) 1601
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 408354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 123259
30.2%
C 120826
29.6%
A 115291
28.2%
E 12348
 
3.0%
R 11412
 
2.8%
S 9080
 
2.2%
U 6057
 
1.5%
D 3868
 
0.9%
V 3359
 
0.8%
N 1253
 
0.3%
Other values (9) 1601
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 408354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 123259
30.2%
C 120826
29.6%
A 115291
28.2%
E 12348
 
3.0%
R 11412
 
2.8%
S 9080
 
2.2%
U 6057
 
1.5%
D 3868
 
0.9%
V 3359
 
0.8%
N 1253
 
0.3%
Other values (9) 1601
 
0.4%
Distinct30621
Distinct (%)22.5%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
2024-08-08T20:32:51.622046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length29
Median length26
Mean length12.333669
Min length6

Characters and Unicode

Total characters1679155
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8742 ?
Unique (%)6.4%

Sample

1st rowScotty Bierce
2nd rowBudge Garrett
3rd rowGeorge Johnson
4th rowAl Nesser
5th rowTommy Tomlin
ValueCountFrequency (%)
mike 2907
 
1.1%
john 2771
 
1.0%
jim 2088
 
0.8%
johnson 1854
 
0.7%
bob 1848
 
0.7%
williams 1847
 
0.7%
smith 1832
 
0.7%
joe 1784
 
0.7%
chris 1694
 
0.6%
jones 1528
 
0.6%
Other values (16742) 253265
92.6%
2024-08-08T20:32:52.009080image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 153151
 
9.1%
137274
 
8.2%
a 124722
 
7.4%
n 121092
 
7.2%
r 117091
 
7.0%
o 105484
 
6.3%
i 95557
 
5.7%
l 84474
 
5.0%
s 66128
 
3.9%
t 57022
 
3.4%
Other values (49) 617160
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1679155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 153151
 
9.1%
137274
 
8.2%
a 124722
 
7.4%
n 121092
 
7.2%
r 117091
 
7.0%
o 105484
 
6.3%
i 95557
 
5.7%
l 84474
 
5.0%
s 66128
 
3.9%
t 57022
 
3.4%
Other values (49) 617160
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1679155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 153151
 
9.1%
137274
 
8.2%
a 124722
 
7.4%
n 121092
 
7.2%
r 117091
 
7.0%
o 105484
 
6.3%
i 95557
 
5.7%
l 84474
 
5.0%
s 66128
 
3.9%
t 57022
 
3.4%
Other values (49) 617160
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1679155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 153151
 
9.1%
137274
 
8.2%
a 124722
 
7.4%
n 121092
 
7.2%
r 117091
 
7.0%
o 105484
 
6.3%
i 95557
 
5.7%
l 84474
 
5.0%
s 66128
 
3.9%
t 57022
 
3.4%
Other values (49) 617160
36.8%
Distinct5104
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:52.377760image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length22
Median length21
Mean length5.8268464
Min length1

Characters and Unicode

Total characters793296
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1031 ?
Unique (%)0.8%

Sample

1st rowBruce
2nd rowAlfred
3rd rowGeorge
4th rowAlfred
5th rowJohn
ValueCountFrequency (%)
john 4026
 
3.0%
james 3883
 
2.8%
robert 3322
 
2.4%
michael 3206
 
2.4%
william 3017
 
2.2%
david 2038
 
1.5%
charles 1933
 
1.4%
thomas 1708
 
1.3%
richard 1649
 
1.2%
joseph 1580
 
1.2%
Other values (5025) 109980
80.7%
2024-08-08T20:32:52.698397image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 82620
 
10.4%
a 76033
 
9.6%
r 65532
 
8.3%
n 60473
 
7.6%
i 49115
 
6.2%
o 48384
 
6.1%
l 43976
 
5.5%
h 32325
 
4.1%
t 28035
 
3.5%
s 26593
 
3.4%
Other values (48) 280210
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 793296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 82620
 
10.4%
a 76033
 
9.6%
r 65532
 
8.3%
n 60473
 
7.6%
i 49115
 
6.2%
o 48384
 
6.1%
l 43976
 
5.5%
h 32325
 
4.1%
t 28035
 
3.5%
s 26593
 
3.4%
Other values (48) 280210
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 793296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 82620
 
10.4%
a 76033
 
9.6%
r 65532
 
8.3%
n 60473
 
7.6%
i 49115
 
6.2%
o 48384
 
6.1%
l 43976
 
5.5%
h 32325
 
4.1%
t 28035
 
3.5%
s 26593
 
3.4%
Other values (48) 280210
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 793296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 82620
 
10.4%
a 76033
 
9.6%
r 65532
 
8.3%
n 60473
 
7.6%
i 49115
 
6.2%
o 48384
 
6.1%
l 43976
 
5.5%
h 32325
 
4.1%
t 28035
 
3.5%
s 26593
 
3.4%
Other values (48) 280210
35.3%
Distinct12684
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:52.953556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length6.411003
Min length3

Characters and Unicode

Total characters872826
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3011 ?
Unique (%)2.2%

Sample

1st rowBierce
2nd rowGarrett
3rd rowJohnson
4th rowNesser
5th rowTomlin
ValueCountFrequency (%)
williams 1847
 
1.4%
johnson 1845
 
1.4%
smith 1830
 
1.3%
jones 1528
 
1.1%
brown 1400
 
1.0%
davis 1017
 
0.7%
jackson 881
 
0.6%
thomas 877
 
0.6%
harris 724
 
0.5%
wilson 682
 
0.5%
Other values (12663) 123973
90.8%
2024-08-08T20:32:53.336124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 84241
 
9.7%
n 69418
 
8.0%
a 67763
 
7.8%
r 66314
 
7.6%
o 64135
 
7.3%
l 53377
 
6.1%
i 51995
 
6.0%
s 49782
 
5.7%
t 36148
 
4.1%
h 24076
 
2.8%
Other values (46) 305577
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 872826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 84241
 
9.7%
n 69418
 
8.0%
a 67763
 
7.8%
r 66314
 
7.6%
o 64135
 
7.3%
l 53377
 
6.1%
i 51995
 
6.0%
s 49782
 
5.7%
t 36148
 
4.1%
h 24076
 
2.8%
Other values (46) 305577
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 872826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 84241
 
9.7%
n 69418
 
8.0%
a 67763
 
7.8%
r 66314
 
7.6%
o 64135
 
7.3%
l 53377
 
6.1%
i 51995
 
6.0%
s 49782
 
5.7%
t 36148
 
4.1%
h 24076
 
2.8%
Other values (46) 305577
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 872826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 84241
 
9.7%
n 69418
 
8.0%
a 67763
 
7.8%
r 66314
 
7.6%
o 64135
 
7.3%
l 53377
 
6.1%
i 51995
 
6.0%
s 49782
 
5.7%
t 36148
 
4.1%
h 24076
 
2.8%
Other values (46) 305577
35.0%

birth_date
Date

MISSING 

Distinct19609
Distinct (%)14.7%
Missing2861
Missing (%)2.1%
Memory size1.0 MiB
Minimum1875-04-25 00:00:00
Maximum2004-01-20 00:00:00
2024-08-08T20:32:53.456856image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:53.593083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

height
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing440
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean73.758358
Minimum61
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:53.715196image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile70
Q172
median74
Q376
95-th percentile78
Maximum84
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5331122
Coefficient of variation (CV)0.034343392
Kurtosis-0.2949307
Mean73.758358
Median Absolute Deviation (MAD)2
Skewness-0.13493979
Sum10009378
Variance6.4166574
MonotonicityNot monotonic
2024-08-08T20:32:53.814288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
74 20380
15.0%
75 19588
14.4%
73 17861
13.1%
72 16008
11.8%
76 15804
11.6%
71 11752
8.6%
77 11203
8.2%
70 8520
6.3%
78 5317
 
3.9%
69 3871
 
2.8%
Other values (13) 5401
 
4.0%
ValueCountFrequency (%)
61 1
 
< 0.1%
63 2
 
< 0.1%
64 17
 
< 0.1%
65 46
 
< 0.1%
66 186
 
0.1%
67 498
 
0.4%
68 1727
 
1.3%
69 3871
 
2.8%
70 8520
6.3%
71 11752
8.6%
ValueCountFrequency (%)
84 1
 
< 0.1%
83 1
 
< 0.1%
82 11
 
< 0.1%
81 150
 
0.1%
80 696
 
0.5%
79 2065
 
1.5%
78 5317
 
3.9%
77 11203
8.2%
76 15804
11.6%
75 19588
14.4%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct244
Distinct (%)0.2%
Missing145
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean232.14927
Minimum0
Maximum1794
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:53.922386image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile180
Q1200
median225
Q3255
95-th percentile314
Maximum1794
Range1794
Interquartile range (IQR)55

Descriptive statistics

Standard deviation42.131178
Coefficient of variation (CV)0.18148314
Kurtosis14.002883
Mean232.14927
Median Absolute Deviation (MAD)29
Skewness0.92506254
Sum31572301
Variance1775.0362
MonotonicityNot monotonic
2024-08-08T20:32:54.046499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190 4755
 
3.5%
195 4218
 
3.1%
200 4183
 
3.1%
250 3772
 
2.8%
210 3627
 
2.7%
205 3492
 
2.6%
220 3297
 
2.4%
185 3293
 
2.4%
215 3262
 
2.4%
230 3220
 
2.4%
Other values (234) 98881
72.6%
ValueCountFrequency (%)
0 16
 
< 0.1%
1 70
0.1%
18 1
 
< 0.1%
119 1
 
< 0.1%
135 2
 
< 0.1%
140 10
 
< 0.1%
142 4
 
< 0.1%
143 3
 
< 0.1%
144 2
 
< 0.1%
145 14
 
< 0.1%
ValueCountFrequency (%)
1794 1
 
< 0.1%
410 2
 
< 0.1%
394 1
 
< 0.1%
390 1
 
< 0.1%
388 2
 
< 0.1%
384 1
 
< 0.1%
382 1
 
< 0.1%
380 13
< 0.1%
379 3
 
< 0.1%
378 2
 
< 0.1%

college
Text

MISSING 

Distinct499
Distinct (%)1.4%
Missing100526
Missing (%)73.8%
Memory size1.0 MiB
2024-08-08T20:32:54.237719image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length28
Median length24
Mean length10.475055
Min length3

Characters and Unicode

Total characters373111
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.1%

Sample

1st rowSouth Dakota State
2nd rowSouth Dakota State
3rd rowTennessee
4th rowSouth Dakota State
5th rowMichigan
ValueCountFrequency (%)
state 7476
 
13.9%
florida 1818
 
3.4%
texas 1635
 
3.0%
carolina 1377
 
2.6%
michigan 1260
 
2.3%
georgia 1018
 
1.9%
north 993
 
1.9%
tech 974
 
1.8%
southern 971
 
1.8%
virginia 952
 
1.8%
Other values (420) 35175
65.6%
2024-08-08T20:32:54.527013image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 43587
 
11.7%
i 30069
 
8.1%
e 29222
 
7.8%
t 29065
 
7.8%
o 25349
 
6.8%
n 24837
 
6.7%
r 19655
 
5.3%
s 19451
 
5.2%
18030
 
4.8%
l 14252
 
3.8%
Other values (49) 119594
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 373111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 43587
 
11.7%
i 30069
 
8.1%
e 29222
 
7.8%
t 29065
 
7.8%
o 25349
 
6.8%
n 24837
 
6.7%
r 19655
 
5.3%
s 19451
 
5.2%
18030
 
4.8%
l 14252
 
3.8%
Other values (49) 119594
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 373111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 43587
 
11.7%
i 30069
 
8.1%
e 29222
 
7.8%
t 29065
 
7.8%
o 25349
 
6.8%
n 24837
 
6.7%
r 19655
 
5.3%
s 19451
 
5.2%
18030
 
4.8%
l 14252
 
3.8%
Other values (49) 119594
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 373111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 43587
 
11.7%
i 30069
 
8.1%
e 29222
 
7.8%
t 29065
 
7.8%
o 25349
 
6.8%
n 24837
 
6.7%
r 19655
 
5.3%
s 19451
 
5.2%
18030
 
4.8%
l 14252
 
3.8%
Other values (49) 119594
32.1%

gsis_id
Text

MISSING 

Distinct17564
Distinct (%)21.1%
Missing53093
Missing (%)39.0%
Memory size1.0 MiB
2024-08-08T20:32:54.769787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters830520
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4113 ?
Unique (%)5.0%

Sample

1st row00-0009716
2nd row00-0009716
3rd row00-0009716
4th row00-0015035
5th row00-0003094
ValueCountFrequency (%)
00-0016098 26
 
< 0.1%
00-0002655 25
 
< 0.1%
00-0000282 25
 
< 0.1%
00-0016919 25
 
< 0.1%
00-0019641 24
 
< 0.1%
00-0000313 23
 
< 0.1%
00-0009584 23
 
< 0.1%
00-0019596 23
 
< 0.1%
00-0008961 22
 
< 0.1%
00-0005113 22
 
< 0.1%
Other values (17554) 82814
99.7%
2024-08-08T20:32:55.099092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 381551
45.9%
- 83052
 
10.0%
2 60651
 
7.3%
3 59347
 
7.1%
1 50590
 
6.1%
4 34381
 
4.1%
6 34078
 
4.1%
5 33468
 
4.0%
7 32478
 
3.9%
9 31717
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 830520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 381551
45.9%
- 83052
 
10.0%
2 60651
 
7.3%
3 59347
 
7.1%
1 50590
 
6.1%
4 34381
 
4.1%
6 34078
 
4.1%
5 33468
 
4.0%
7 32478
 
3.9%
9 31717
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 830520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 381551
45.9%
- 83052
 
10.0%
2 60651
 
7.3%
3 59347
 
7.1%
1 50590
 
6.1%
4 34381
 
4.1%
6 34078
 
4.1%
5 33468
 
4.0%
7 32478
 
3.9%
9 31717
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 830520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 381551
45.9%
- 83052
 
10.0%
2 60651
 
7.3%
3 59347
 
7.1%
1 50590
 
6.1%
4 34381
 
4.1%
6 34078
 
4.1%
5 33468
 
4.0%
7 32478
 
3.9%
9 31717
 
3.8%

espn_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3807
Distinct (%)16.5%
Missing113048
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean1858667.5
Minimum1097
Maximum4917635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:55.222213image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1097
5-th percentile8416
Q114297
median2576480
Q33124086
95-th percentile4242335
Maximum4917635
Range4916538
Interquartile range (IQR)3109789

Descriptive statistics

Standard deviation1732333.7
Coefficient of variation (CV)0.93202992
Kurtosis-1.7543551
Mean1858667.5
Median Absolute Deviation (MAD)1666036
Skewness0.0027841108
Sum4.2929643 × 1010
Variance3.00098 × 1012
MonotonicityNot monotonic
2024-08-08T20:32:55.349330image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1097 25
 
< 0.1%
2330 23
 
< 0.1%
2580 21
 
< 0.1%
8439 20
 
< 0.1%
1440 20
 
< 0.1%
5713 19
 
< 0.1%
4333 19
 
< 0.1%
6012 19
 
< 0.1%
2273 19
 
< 0.1%
11122 19
 
< 0.1%
Other values (3797) 22893
 
16.8%
(Missing) 113048
83.0%
ValueCountFrequency (%)
1097 25
< 0.1%
1231 17
< 0.1%
1428 18
< 0.1%
1430 13
< 0.1%
1433 16
< 0.1%
1440 20
< 0.1%
1490 13
< 0.1%
1575 17
< 0.1%
1756 11
< 0.1%
1858 15
< 0.1%
ValueCountFrequency (%)
4917635 2
< 0.1%
4820592 1
 
< 0.1%
4820589 2
< 0.1%
4697815 3
< 0.1%
4697639 2
< 0.1%
4692025 1
 
< 0.1%
4689546 3
< 0.1%
4686470 1
 
< 0.1%
4686421 4
< 0.1%
4685721 1
 
< 0.1%

sportradar_id
Text

MISSING 

Distinct4277
Distinct (%)17.7%
Missing112023
Missing (%)82.3%
Memory size1.0 MiB
2024-08-08T20:32:55.515908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters868392
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)0.6%

Sample

1st row9ecf8040-10f9-4a5c-92da-1b4d77bd6760
2nd row382154cf-7cc6-494c-8426-9f78aa4c4b90
3rd row9ecf8040-10f9-4a5c-92da-1b4d77bd6760
4th rowa725e7c5-86df-4b5b-abe0-71b809be988d
5th row382154cf-7cc6-494c-8426-9f78aa4c4b90
ValueCountFrequency (%)
9ecf8040-10f9-4a5c-92da-1b4d77bd6760 25
 
0.1%
41c44740-d0f6-44ab-8347-3b5d515e5ecf 23
 
0.1%
bb5957e6-ce7d-47ab-8036-22191ffc1c44 21
 
0.1%
0ce48193-e2fa-466e-a986-33f751add206 20
 
0.1%
e5247e5f-c4af-4a9b-8c7c-da75ef7fbf8d 20
 
0.1%
c4b15bec-4adf-444a-bda1-5a07ade70abf 19
 
0.1%
edaad8e3-62cd-4715-b225-0010ee9825a0 19
 
0.1%
218d1644-603e-4da3-9ce1-48ce3927494f 19
 
0.1%
480277d1-47c9-44df-969e-038a84cd0fea 19
 
0.1%
46aab8e6-3ca9-4213-a6cb-87db90786f6b 19
 
0.1%
Other values (4267) 23918
99.2%
2024-08-08T20:32:55.771123image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 96488
 
11.1%
4 69201
 
8.0%
b 51215
 
5.9%
9 50744
 
5.8%
a 50266
 
5.8%
8 50063
 
5.8%
c 46598
 
5.4%
f 46564
 
5.4%
1 45808
 
5.3%
3 45595
 
5.3%
Other values (7) 315850
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 868392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 96488
 
11.1%
4 69201
 
8.0%
b 51215
 
5.9%
9 50744
 
5.8%
a 50266
 
5.8%
8 50063
 
5.8%
c 46598
 
5.4%
f 46564
 
5.4%
1 45808
 
5.3%
3 45595
 
5.3%
Other values (7) 315850
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 868392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 96488
 
11.1%
4 69201
 
8.0%
b 51215
 
5.9%
9 50744
 
5.8%
a 50266
 
5.8%
8 50063
 
5.8%
c 46598
 
5.4%
f 46564
 
5.4%
1 45808
 
5.3%
3 45595
 
5.3%
Other values (7) 315850
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 868392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 96488
 
11.1%
4 69201
 
8.0%
b 51215
 
5.9%
9 50744
 
5.8%
a 50266
 
5.8%
8 50063
 
5.8%
c 46598
 
5.4%
f 46564
 
5.4%
1 45808
 
5.3%
3 45595
 
5.3%
Other values (7) 315850
36.4%

yahoo_id
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct3960
Distinct (%)17.8%
Missing113891
Missing (%)83.7%
Infinite0
Infinite (%)0.0%
Mean27720.702
Minimum3727
Maximum900125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:55.887829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3727
5-th percentile7799.95
Q125871
median29253
Q331503
95-th percentile33697.4
Maximum900125
Range896398
Interquartile range (IQR)5632

Descriptive statistics

Standard deviation28553.077
Coefficient of variation (CV)1.0300272
Kurtosis858.54644
Mean27720.702
Median Absolute Deviation (MAD)2618
Skewness28.153708
Sum6.168965 × 108
Variance8.1527823 × 108
MonotonicityNot monotonic
2024-08-08T20:32:56.008434image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3727 25
 
< 0.1%
5228 23
 
< 0.1%
5479 21
 
< 0.1%
4269 20
 
< 0.1%
7200 20
 
< 0.1%
6947 19
 
< 0.1%
7122 19
 
< 0.1%
6243 19
 
< 0.1%
8565 19
 
< 0.1%
5171 19
 
< 0.1%
Other values (3950) 22050
 
16.2%
(Missing) 113891
83.7%
ValueCountFrequency (%)
3727 25
< 0.1%
4256 18
< 0.1%
4269 20
< 0.1%
4416 17
< 0.1%
5046 19
< 0.1%
5171 19
< 0.1%
5228 23
< 0.1%
5448 14
< 0.1%
5477 14
< 0.1%
5479 21
< 0.1%
ValueCountFrequency (%)
900125 4
< 0.1%
900039 6
< 0.1%
900035 8
< 0.1%
900026 4
< 0.1%
40819 2
 
< 0.1%
40780 2
 
< 0.1%
40779 2
 
< 0.1%
40756 1
 
< 0.1%
40748 2
 
< 0.1%
40742 2
 
< 0.1%

rotowire_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4295
Distinct (%)17.7%
Missing111909
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean10436.626
Minimum395
Maximum36732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:56.120042image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum395
5-th percentile4345
Q18029
median10717.5
Q313280.5
95-th percentile15877
Maximum36732
Range36337
Interquartile range (IQR)5251.5

Descriptive statistics

Standard deviation3630.6572
Coefficient of variation (CV)0.34787652
Kurtosis-0.31653148
Mean10436.626
Median Absolute Deviation (MAD)2660.5
Skewness-0.25500371
Sum2.5294208 × 108
Variance13181672
MonotonicityNot monotonic
2024-08-08T20:32:56.245538image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395 25
 
< 0.1%
1350 23
 
< 0.1%
2178 21
 
< 0.1%
994 20
 
< 0.1%
4307 20
 
< 0.1%
1299 19
 
< 0.1%
2832 19
 
< 0.1%
3874 19
 
< 0.1%
5051 19
 
< 0.1%
7123 19
 
< 0.1%
Other values (4285) 24032
 
17.7%
(Missing) 111909
82.2%
ValueCountFrequency (%)
395 25
< 0.1%
721 17
< 0.1%
902 18
< 0.1%
906 13
< 0.1%
909 16
< 0.1%
945 13
< 0.1%
949 17
< 0.1%
994 20
< 0.1%
1127 11
< 0.1%
1214 15
< 0.1%
ValueCountFrequency (%)
36732 2
< 0.1%
28045 2
< 0.1%
23362 2
< 0.1%
17649 2
< 0.1%
17626 2
< 0.1%
17624 2
< 0.1%
17620 1
< 0.1%
17611 1
< 0.1%
17604 2
< 0.1%
17595 2
< 0.1%

pff_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3541
Distinct (%)15.8%
Missing113745
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean19377.284
Minimum226
Maximum143793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:56.366304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum226
5-th percentile2130
Q16332
median9586.5
Q333007
95-th percentile56308.9
Maximum143793
Range143567
Interquartile range (IQR)26675

Descriptive statistics

Standard deviation20415.14
Coefficient of variation (CV)1.0535605
Kurtosis2.1272304
Mean19377.284
Median Absolute Deviation (MAD)3723.5
Skewness1.5455258
Sum4.3405115 × 108
Variance4.1677794 × 108
MonotonicityNot monotonic
2024-08-08T20:32:56.490419image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333 26
 
< 0.1%
226 25
 
< 0.1%
585 24
 
< 0.1%
698 23
 
< 0.1%
802 21
 
< 0.1%
422 20
 
< 0.1%
2241 20
 
< 0.1%
2148 19
 
< 0.1%
1223 19
 
< 0.1%
3471 19
 
< 0.1%
Other values (3531) 22184
 
16.3%
(Missing) 113745
83.5%
ValueCountFrequency (%)
226 25
< 0.1%
237 17
< 0.1%
327 18
< 0.1%
333 26
< 0.1%
342 16
< 0.1%
367 13
< 0.1%
408 17
< 0.1%
422 20
< 0.1%
440 11
< 0.1%
498 15
< 0.1%
ValueCountFrequency (%)
143793 2
 
< 0.1%
143780 4
< 0.1%
143769 4
< 0.1%
143358 4
< 0.1%
136106 2
 
< 0.1%
117753 4
< 0.1%
115664 5
< 0.1%
113168 3
< 0.1%
109795 5
< 0.1%
108840 5
< 0.1%

pfr_id
Text

MISSING 

Distinct3078
Distinct (%)17.7%
Missing118709
Missing (%)87.2%
Memory size1.0 MiB
2024-08-08T20:32:56.714008image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.0041294
Min length8

Characters and Unicode

Total characters139560
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)0.6%

Sample

1st rowGonzTo00
2nd rowBatcCh00
3rd rowGonzTo00
4th rowMannPe00
5th rowMossRa00
ValueCountFrequency (%)
bradto00 23
 
0.1%
breedr00 21
 
0.1%
rodgaa00 20
 
0.1%
leexan20 19
 
0.1%
lechsh20 19
 
0.1%
janikseb01 19
 
0.1%
mannpe00 18
 
0.1%
mccojo01 18
 
0.1%
lewima00 18
 
0.1%
johnjo05 18
 
0.1%
Other values (3068) 17243
98.9%
2024-08-08T20:32:57.183046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29329
21.0%
a 10615
 
7.6%
e 7645
 
5.5%
o 7236
 
5.2%
r 5984
 
4.3%
i 5250
 
3.8%
l 5148
 
3.7%
J 3991
 
2.9%
n 3945
 
2.8%
M 3042
 
2.2%
Other values (55) 57375
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29329
21.0%
a 10615
 
7.6%
e 7645
 
5.5%
o 7236
 
5.2%
r 5984
 
4.3%
i 5250
 
3.8%
l 5148
 
3.7%
J 3991
 
2.9%
n 3945
 
2.8%
M 3042
 
2.2%
Other values (55) 57375
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29329
21.0%
a 10615
 
7.6%
e 7645
 
5.5%
o 7236
 
5.2%
r 5984
 
4.3%
i 5250
 
3.8%
l 5148
 
3.7%
J 3991
 
2.9%
n 3945
 
2.8%
M 3042
 
2.2%
Other values (55) 57375
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29329
21.0%
a 10615
 
7.6%
e 7645
 
5.5%
o 7236
 
5.2%
r 5984
 
4.3%
i 5250
 
3.8%
l 5148
 
3.7%
J 3991
 
2.9%
n 3945
 
2.8%
M 3042
 
2.2%
Other values (55) 57375
41.1%

fantasy_data_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3218
Distinct (%)15.4%
Missing115259
Missing (%)84.7%
Infinite0
Infinite (%)0.0%
Mean16031.862
Minimum3
Maximum22477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:57.302107image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4633
Q113870
median16938.5
Q319872.75
95-th percentile21881
Maximum22477
Range22474
Interquartile range (IQR)6002.75

Descriptive statistics

Standard deviation4995.3544
Coefficient of variation (CV)0.31158915
Kurtosis0.92495909
Mean16031.862
Median Absolute Deviation (MAD)2961.5
Skewness-1.1750023
Sum3.3484148 × 108
Variance24953566
MonotonicityNot monotonic
2024-08-08T20:32:57.420063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3258 25
 
< 0.1%
4314 23
 
< 0.1%
7242 21
 
< 0.1%
5714 20
 
< 0.1%
2593 20
 
< 0.1%
8920 19
 
< 0.1%
3253 19
 
< 0.1%
7606 19
 
< 0.1%
3100 19
 
< 0.1%
549 19
 
< 0.1%
Other values (3208) 20682
 
15.2%
(Missing) 115259
84.7%
ValueCountFrequency (%)
3 9
< 0.1%
204 11
< 0.1%
292 14
< 0.1%
430 17
< 0.1%
462 6
 
< 0.1%
547 6
 
< 0.1%
549 19
< 0.1%
611 17
< 0.1%
722 17
< 0.1%
732 16
< 0.1%
ValueCountFrequency (%)
22477 4
< 0.1%
22475 3
< 0.1%
22465 1
 
< 0.1%
22449 5
< 0.1%
22442 5
< 0.1%
22441 3
< 0.1%
22436 3
< 0.1%
22434 5
< 0.1%
22429 5
< 0.1%
22427 3
< 0.1%

sleeper_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4142
Distinct (%)17.8%
Missing112839
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean3620.5775
Minimum4
Maximum11533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:57.536079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile199
Q11201
median3222
Q35844
95-th percentile8179.75
Maximum11533
Range11529
Interquartile range (IQR)4643

Descriptive statistics

Standard deviation2723.7293
Coefficient of variation (CV)0.75229139
Kurtosis-0.57386484
Mean3620.5775
Median Absolute Deviation (MAD)2156
Skewness0.5662666
Sum84381179
Variance7418701.1
MonotonicityNot monotonic
2024-08-08T20:32:57.660198image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 25
 
< 0.1%
167 23
 
< 0.1%
289 21
 
< 0.1%
229 20
 
< 0.1%
96 20
 
< 0.1%
17 19
 
< 0.1%
412 19
 
< 0.1%
112 19
 
< 0.1%
127 19
 
< 0.1%
307 19
 
< 0.1%
Other values (4132) 23102
 
17.0%
(Missing) 112839
82.9%
ValueCountFrequency (%)
4 11
< 0.1%
6 14
< 0.1%
13 17
< 0.1%
16 6
 
< 0.1%
17 19
< 0.1%
19 17
< 0.1%
23 17
< 0.1%
24 16
< 0.1%
25 10
< 0.1%
32 9
< 0.1%
ValueCountFrequency (%)
11533 2
< 0.1%
11528 1
< 0.1%
11514 2
< 0.1%
11512 2
< 0.1%
11510 2
< 0.1%
11505 2
< 0.1%
11489 2
< 0.1%
11487 2
< 0.1%
11483 2
< 0.1%
11479 2
< 0.1%

years_exp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)0.1%
Missing79743
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean3.3875572
Minimum0
Maximum41
Zeros10884
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:57.775687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum41
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2337343
Coefficient of variation (CV)0.95459181
Kurtosis3.3663953
Mean3.3875572
Median Absolute Deviation (MAD)2
Skewness1.3750976
Sum191065
Variance10.457038
MonotonicityNot monotonic
2024-08-08T20:32:57.878074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 10884
 
8.0%
1 9066
 
6.7%
2 7783
 
5.7%
3 6528
 
4.8%
4 5276
 
3.9%
5 4284
 
3.1%
6 3435
 
2.5%
7 2641
 
1.9%
8 2017
 
1.5%
9 1473
 
1.1%
Other values (23) 3015
 
2.2%
(Missing) 79743
58.6%
ValueCountFrequency (%)
0 10884
8.0%
1 9066
6.7%
2 7783
5.7%
3 6528
4.8%
4 5276
3.9%
5 4284
 
3.1%
6 3435
 
2.5%
7 2641
 
1.9%
8 2017
 
1.5%
9 1473
 
1.1%
ValueCountFrequency (%)
41 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 2
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
29 1
< 0.1%
25 1
< 0.1%
24 2
< 0.1%
23 2
< 0.1%

headshot_url
Text

MISSING 

Distinct37359
Distinct (%)27.9%
Missing2124
Missing (%)1.6%
Memory size1.0 MiB
2024-08-08T20:32:58.074258image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length82
Median length82
Mean length81.99847
Min length80

Characters and Unicode

Total characters10989517
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13873 ?
Unique (%)10.4%

Sample

1st rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/fdvmtlugumgyrxww1uzu
2nd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/sdjs03sggqwf0ca8qhzp
3rd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/fahqn7dy7n3kvwcrx0do
4th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/xew88uwps7tcuzhzpn99
5th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/xocsljjtywb3nkms16la
ValueCountFrequency (%)
https://static.www.nfl.com/image/private/f_auto,q_auto/league/aofp1gfh2uwiundjxs4h 27
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/hj3mtwjx0byr01a7463p 26
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/vayogcgph7k6j3uhza5d 25
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/nk87h6cjuij14iofd5ix 25
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/npgauvcjnh8dvxadcrpy 25
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/imfj1hl4kob4jof8hcwa 25
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/bmbhldch291mziip7hm4 24
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/fofzz3dwrg1vr2drmoak 23
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/ck6so8zwqjsmxhdabvog 23
 
< 0.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/ypoocygzfeucbr8sqat0 22
 
< 0.1%
Other values (37349) 133776
99.8%
2024-08-08T20:32:58.395558image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1026169
 
9.3%
/ 938147
 
8.5%
a 891570
 
8.1%
e 622521
 
5.7%
w 490228
 
4.5%
o 489732
 
4.5%
i 489029
 
4.4%
u 488078
 
4.4%
. 402063
 
3.7%
c 356425
 
3.2%
Other values (31) 4795555
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10989517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1026169
 
9.3%
/ 938147
 
8.5%
a 891570
 
8.1%
e 622521
 
5.7%
w 490228
 
4.5%
o 489732
 
4.5%
i 489029
 
4.4%
u 488078
 
4.4%
. 402063
 
3.7%
c 356425
 
3.2%
Other values (31) 4795555
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10989517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1026169
 
9.3%
/ 938147
 
8.5%
a 891570
 
8.1%
e 622521
 
5.7%
w 490228
 
4.5%
o 489732
 
4.5%
i 489029
 
4.4%
u 488078
 
4.4%
. 402063
 
3.7%
c 356425
 
3.2%
Other values (31) 4795555
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10989517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1026169
 
9.3%
/ 938147
 
8.5%
a 891570
 
8.1%
e 622521
 
5.7%
w 490228
 
4.5%
o 489732
 
4.5%
i 489029
 
4.4%
u 488078
 
4.4%
. 402063
 
3.7%
c 356425
 
3.2%
Other values (31) 4795555
43.6%

esb_id
Text

MISSING 

Distinct17407
Distinct (%)21.0%
Missing53258
Missing (%)39.1%
Memory size1.0 MiB
2024-08-08T20:32:58.628901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters745983
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3955 ?
Unique (%)4.8%

Sample

1st rowLEA363377
2nd rowLEA363377
3rd rowLEA363377
4th rowSLA363377
5th rowCLA772560
ValueCountFrequency (%)
tay220162 26
 
< 0.1%
and020258 25
 
< 0.1%
car243867 25
 
< 0.1%
vin196019 25
 
< 0.1%
jon755755 24
 
< 0.1%
bra371156 23
 
< 0.1%
and273108 23
 
< 0.1%
lan106026 23
 
< 0.1%
fea207645 22
 
< 0.1%
jun498348 22
 
< 0.1%
Other values (17397) 82649
99.7%
2024-08-08T20:32:58.951250image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 54926
 
7.4%
6 54237
 
7.3%
1 53968
 
7.2%
0 53397
 
7.2%
4 51977
 
7.0%
5 51973
 
7.0%
7 48522
 
6.5%
3 47731
 
6.4%
8 40845
 
5.5%
9 39746
 
5.3%
Other values (26) 248661
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 745983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 54926
 
7.4%
6 54237
 
7.3%
1 53968
 
7.2%
0 53397
 
7.2%
4 51977
 
7.0%
5 51973
 
7.0%
7 48522
 
6.5%
3 47731
 
6.4%
8 40845
 
5.5%
9 39746
 
5.3%
Other values (26) 248661
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 745983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 54926
 
7.4%
6 54237
 
7.3%
1 53968
 
7.2%
0 53397
 
7.2%
4 51977
 
7.0%
5 51973
 
7.0%
7 48522
 
6.5%
3 47731
 
6.4%
8 40845
 
5.5%
9 39746
 
5.3%
Other values (26) 248661
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 745983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 54926
 
7.4%
6 54237
 
7.3%
1 53968
 
7.2%
0 53397
 
7.2%
4 51977
 
7.0%
5 51973
 
7.0%
7 48522
 
6.5%
3 47731
 
6.4%
8 40845
 
5.5%
9 39746
 
5.3%
Other values (26) 248661
33.3%

gsis_it_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8556
Distinct (%)23.8%
Missing100174
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean43412.353
Minimum9143
Maximum57973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:59.072369image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum9143
5-th percentile30857
Q138613
median43374
Q347917
95-th percentile55875
Maximum57973
Range48830
Interquartile range (IQR)9304

Descriptive statistics

Standard deviation7389.3379
Coefficient of variation (CV)0.17021279
Kurtosis-0.48969392
Mean43412.353
Median Absolute Deviation (MAD)4657
Skewness-0.055419036
Sum1.5615858 × 109
Variance54602314
MonotonicityNot monotonic
2024-08-08T20:32:59.198497image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21213 25
 
< 0.1%
25511 23
 
< 0.1%
26250 21
 
< 0.1%
29851 20
 
< 0.1%
23860 20
 
< 0.1%
25451 19
 
< 0.1%
29550 19
 
< 0.1%
27091 19
 
< 0.1%
31446 19
 
< 0.1%
29141 19
 
< 0.1%
Other values (8546) 35767
 
26.3%
(Missing) 100174
73.6%
ValueCountFrequency (%)
9143 5
 
< 0.1%
17623 1
 
< 0.1%
21213 25
< 0.1%
23446 18
< 0.1%
23449 18
< 0.1%
23636 17
< 0.1%
23860 20
< 0.1%
25326 19
< 0.1%
25451 19
< 0.1%
25511 23
< 0.1%
ValueCountFrequency (%)
57973 1
< 0.1%
57962 1
< 0.1%
57935 1
< 0.1%
57932 1
< 0.1%
57931 1
< 0.1%
57930 1
< 0.1%
57929 1
< 0.1%
57928 1
< 0.1%
57927 1
< 0.1%
57926 1
< 0.1%

smart_id
Text

MISSING 

Distinct17377
Distinct (%)21.0%
Missing53283
Missing (%)39.1%
Memory size1.0 MiB
2024-08-08T20:32:59.365652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters2983032
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3933 ?
Unique (%)4.7%

Sample

1st row32004c45-4136-3377-2de6-ed68209b7976
2nd row32004c45-4136-3377-2de6-ed68209b7976
3rd row32004c45-4136-3377-2de6-ed68209b7976
4th row3200534c-4136-3377-13fe-7f321cb0819c
5th row3200434c-4177-2560-2161-063befb14937
ValueCountFrequency (%)
32005441-5922-0162-53b0-83184c953269 26
 
< 0.1%
32005649-4e19-6019-e626-0b58f9aa81e1 25
 
< 0.1%
32004341-5224-3867-1223-418051f03b99 25
 
< 0.1%
3200414e-4402-0258-af5e-f904d4733d82 25
 
< 0.1%
32004a4f-4e75-5755-8f93-be08855fe9b0 24
 
< 0.1%
32004252-4137-1156-7ed0-8b9e44948f13 23
 
< 0.1%
3200414e-4427-3108-1809-a396fee76085 23
 
< 0.1%
32004c41-4e10-6026-9cbb-d48fb01c2306 23
 
< 0.1%
32004645-4120-7645-e4e2-23d46bf06e49 22
 
< 0.1%
32004a55-4e49-8348-c78d-09386823c18a 22
 
< 0.1%
Other values (17367) 82624
99.7%
2024-08-08T20:32:59.621897image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 331448
11.1%
4 329773
11.1%
0 302056
10.1%
2 250902
 
8.4%
3 236376
 
7.9%
5 229030
 
7.7%
1 165586
 
5.6%
7 145086
 
4.9%
6 143612
 
4.8%
9 141052
 
4.7%
Other values (7) 708111
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2983032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 331448
11.1%
4 329773
11.1%
0 302056
10.1%
2 250902
 
8.4%
3 236376
 
7.9%
5 229030
 
7.7%
1 165586
 
5.6%
7 145086
 
4.9%
6 143612
 
4.8%
9 141052
 
4.7%
Other values (7) 708111
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2983032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 331448
11.1%
4 329773
11.1%
0 302056
10.1%
2 250902
 
8.4%
3 236376
 
7.9%
5 229030
 
7.7%
1 165586
 
5.6%
7 145086
 
4.9%
6 143612
 
4.8%
9 141052
 
4.7%
Other values (7) 708111
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2983032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 331448
11.1%
4 329773
11.1%
0 302056
10.1%
2 250902
 
8.4%
3 236376
 
7.9%
5 229030
 
7.7%
1 165586
 
5.6%
7 145086
 
4.9%
6 143612
 
4.8%
9 141052
 
4.7%
Other values (7) 708111
23.7%

entry_year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)0.1%
Missing79743
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean2010.6869
Minimum1972
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:59.740005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1972
5-th percentile1998
Q12005
median2011
Q32017
95-th percentile2022
Maximum2024
Range52
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.6457434
Coefficient of variation (CV)0.0038025529
Kurtosis-0.67022443
Mean2010.6869
Median Absolute Deviation (MAD)6
Skewness-0.33158798
Sum1.1340676 × 108
Variance58.457392
MonotonicityNot monotonic
2024-08-08T20:32:59.861989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2018 2940
 
2.2%
2017 2933
 
2.2%
2016 2889
 
2.1%
2019 2644
 
1.9%
2015 2464
 
1.8%
2013 2402
 
1.8%
2014 2401
 
1.8%
2012 2365
 
1.7%
2020 2198
 
1.6%
2010 2177
 
1.6%
Other values (34) 30989
 
22.8%
(Missing) 79743
58.6%
ValueCountFrequency (%)
1972 1
 
< 0.1%
1974 6
 
< 0.1%
1982 8
 
< 0.1%
1983 2
 
< 0.1%
1985 17
 
< 0.1%
1986 7
 
< 0.1%
1987 26
< 0.1%
1988 27
< 0.1%
1989 21
 
< 0.1%
1990 53
< 0.1%
ValueCountFrequency (%)
2024 677
 
0.5%
2023 1144
 
0.8%
2022 1624
1.2%
2021 1510
1.1%
2020 2198
1.6%
2019 2644
1.9%
2018 2940
2.2%
2017 2933
2.2%
2016 2889
2.1%
2015 2464
1.8%

rookie_year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)0.1%
Missing79755
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean2010.6897
Minimum1900
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:32:59.980100image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1998
Q12005
median2012
Q32017
95-th percentile2022
Maximum2024
Range124
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.661855
Coefficient of variation (CV)0.0038105607
Kurtosis0.085118842
Mean2010.6897
Median Absolute Deviation (MAD)6
Skewness-0.38220128
Sum1.1338279 × 108
Variance58.704022
MonotonicityNot monotonic
2024-08-08T20:33:00.104215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2017 2961
 
2.2%
2018 2926
 
2.1%
2016 2879
 
2.1%
2019 2651
 
1.9%
2015 2463
 
1.8%
2014 2407
 
1.8%
2013 2389
 
1.8%
2012 2373
 
1.7%
2010 2171
 
1.6%
2011 2169
 
1.6%
Other values (35) 31001
 
22.8%
(Missing) 79755
58.6%
ValueCountFrequency (%)
1900 1
 
< 0.1%
1972 1
 
< 0.1%
1974 6
 
< 0.1%
1982 8
 
< 0.1%
1983 2
 
< 0.1%
1985 17
< 0.1%
1986 7
 
< 0.1%
1987 26
< 0.1%
1988 27
< 0.1%
1989 21
< 0.1%
ValueCountFrequency (%)
2024 681
 
0.5%
2023 1156
 
0.8%
2022 1644
1.2%
2021 1505
1.1%
2020 2165
1.6%
2019 2651
1.9%
2018 2926
2.1%
2017 2961
2.2%
2016 2879
2.1%
2015 2463
1.8%

draft_club
Categorical

MISSING 

Distinct43
Distinct (%)0.1%
Missing100288
Missing (%)73.7%
Memory size1.0 MiB
GB
 
1384
SF
 
1288
CIN
 
1261
SEA
 
1258
NE
 
1240
Other values (38)
29426 

Length

Max length3
Median length3
Mean length2.752991
Min length2

Characters and Unicode

Total characters98714
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIND
2nd rowOAK
3rd rowGB
4th rowIND
5th rowOAK

Common Values

ValueCountFrequency (%)
GB 1384
 
1.0%
SF 1288
 
0.9%
CIN 1261
 
0.9%
SEA 1258
 
0.9%
NE 1240
 
0.9%
PHI 1215
 
0.9%
DAL 1187
 
0.9%
PIT 1176
 
0.9%
MIN 1168
 
0.9%
IND 1123
 
0.8%
Other values (33) 23557
 
17.3%
(Missing) 100288
73.7%

Length

2024-08-08T20:33:00.216319image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb 1384
 
3.9%
sf 1288
 
3.6%
cin 1261
 
3.5%
sea 1258
 
3.5%
ne 1240
 
3.5%
phi 1215
 
3.4%
dal 1187
 
3.3%
pit 1176
 
3.3%
min 1168
 
3.3%
ind 1123
 
3.1%
Other values (33) 23557
65.7%

Most occurring characters

ValueCountFrequency (%)
A 10928
 
11.1%
N 9999
 
10.1%
I 8540
 
8.7%
T 6787
 
6.9%
E 6279
 
6.4%
L 6136
 
6.2%
C 5775
 
5.9%
S 5666
 
5.7%
D 5264
 
5.3%
B 4822
 
4.9%
Other values (15) 28518
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10928
 
11.1%
N 9999
 
10.1%
I 8540
 
8.7%
T 6787
 
6.9%
E 6279
 
6.4%
L 6136
 
6.2%
C 5775
 
5.9%
S 5666
 
5.7%
D 5264
 
5.3%
B 4822
 
4.9%
Other values (15) 28518
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10928
 
11.1%
N 9999
 
10.1%
I 8540
 
8.7%
T 6787
 
6.9%
E 6279
 
6.4%
L 6136
 
6.2%
C 5775
 
5.9%
S 5666
 
5.7%
D 5264
 
5.3%
B 4822
 
4.9%
Other values (15) 28518
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10928
 
11.1%
N 9999
 
10.1%
I 8540
 
8.7%
T 6787
 
6.9%
E 6279
 
6.4%
L 6136
 
6.2%
C 5775
 
5.9%
S 5666
 
5.7%
D 5264
 
5.3%
B 4822
 
4.9%
Other values (15) 28518
28.9%

ngs_position
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)0.2%
Missing126663
Missing (%)93.0%
Memory size1.0 MiB
INTERIOR_LINE
1183 
EDGE
1116 
WR
876 
TE
746 
MLB
744 
Other values (12)
4817 

Length

Max length13
Median length8
Mean length4.2584898
Min length1

Characters and Unicode

Total characters40379
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowQB
2nd rowWR
3rd rowQB
4th rowQB
5th rowEDGE

Common Values

ValueCountFrequency (%)
INTERIOR_LINE 1183
 
0.9%
EDGE 1116
 
0.8%
WR 876
 
0.6%
TE 746
 
0.5%
MLB 744
 
0.5%
CB 687
 
0.5%
RB 687
 
0.5%
SAFETY 638
 
0.5%
G 585
 
0.4%
T 574
 
0.4%
Other values (7) 1646
 
1.2%
(Missing) 126663
93.0%

Length

2024-08-08T20:33:00.318412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
interior_line 1183
12.5%
edge 1116
11.8%
wr 876
9.2%
te 746
7.9%
mlb 744
7.8%
cb 687
7.2%
rb 687
7.2%
safety 638
 
6.7%
g 585
 
6.2%
t 574
 
6.1%
Other values (7) 1646
17.4%

Most occurring characters

ValueCountFrequency (%)
E 5983
14.8%
R 4393
10.9%
T 3979
9.9%
I 3549
8.8%
B 3026
 
7.5%
L 2881
 
7.1%
N 2366
 
5.9%
O 2137
 
5.3%
_ 2021
 
5.0%
G 1701
 
4.2%
Other values (10) 8343
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 5983
14.8%
R 4393
10.9%
T 3979
9.9%
I 3549
8.8%
B 3026
 
7.5%
L 2881
 
7.1%
N 2366
 
5.9%
O 2137
 
5.3%
_ 2021
 
5.0%
G 1701
 
4.2%
Other values (10) 8343
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 5983
14.8%
R 4393
10.9%
T 3979
9.9%
I 3549
8.8%
B 3026
 
7.5%
L 2881
 
7.1%
N 2366
 
5.9%
O 2137
 
5.3%
_ 2021
 
5.0%
G 1701
 
4.2%
Other values (10) 8343
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 5983
14.8%
R 4393
10.9%
T 3979
9.9%
I 3549
8.8%
B 3026
 
7.5%
L 2881
 
7.1%
N 2366
 
5.9%
O 2137
 
5.3%
_ 2021
 
5.0%
G 1701
 
4.2%
Other values (10) 8343
20.7%

week
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)< 0.1%
Missing79239
Missing (%)58.2%
Infinite0
Infinite (%)0.0%
Mean15.74131
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:33:00.415499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q117
median17
Q318
95-th percentile21
Maximum22
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.4074391
Coefficient of variation (CV)0.343519
Kurtosis2.4649262
Mean15.74131
Median Absolute Deviation (MAD)1
Skewness-1.9238313
Sum895775
Variance29.240398
MonotonicityNot monotonic
2024-08-08T20:33:00.650823image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17 23742
 
17.4%
18 9509
 
7.0%
19 6339
 
4.7%
1 4696
 
3.4%
20 3408
 
2.5%
21 2900
 
2.1%
22 500
 
0.4%
15 478
 
0.4%
16 476
 
0.3%
14 467
 
0.3%
Other values (12) 4391
 
3.2%
(Missing) 79239
58.2%
ValueCountFrequency (%)
1 4696
3.4%
2 381
 
0.3%
3 275
 
0.2%
4 288
 
0.2%
5 345
 
0.3%
6 345
 
0.3%
7 384
 
0.3%
8 350
 
0.3%
9 364
 
0.3%
10 382
 
0.3%
ValueCountFrequency (%)
22 500
 
0.4%
21 2900
 
2.1%
20 3408
 
2.5%
19 6339
 
4.7%
18 9509
7.0%
17 23742
17.4%
16 476
 
0.3%
15 478
 
0.4%
14 467
 
0.3%
13 431
 
0.3%

game_type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing79239
Missing (%)58.2%
Memory size1.0 MiB
REG
38755 
WC
6486 
DIV
5828 
CON
 
2932
SB
 
2905

Length

Max length3
Median length3
Mean length2.8349735
Min length2

Characters and Unicode

Total characters161327
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowDIV
3rd rowCON
4th rowREG
5th rowSB

Common Values

ValueCountFrequency (%)
REG 38755
28.5%
WC 6486
 
4.8%
DIV 5828
 
4.3%
CON 2932
 
2.2%
SB 2905
 
2.1%
(Missing) 79239
58.2%

Length

2024-08-08T20:33:00.747913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:33:00.836995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
reg 38755
68.1%
wc 6486
 
11.4%
div 5828
 
10.2%
con 2932
 
5.2%
sb 2905
 
5.1%

Most occurring characters

ValueCountFrequency (%)
R 38755
24.0%
E 38755
24.0%
G 38755
24.0%
C 9418
 
5.8%
W 6486
 
4.0%
D 5828
 
3.6%
I 5828
 
3.6%
V 5828
 
3.6%
O 2932
 
1.8%
N 2932
 
1.8%
Other values (2) 5810
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 161327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 38755
24.0%
E 38755
24.0%
G 38755
24.0%
C 9418
 
5.8%
W 6486
 
4.0%
D 5828
 
3.6%
I 5828
 
3.6%
V 5828
 
3.6%
O 2932
 
1.8%
N 2932
 
1.8%
Other values (2) 5810
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 161327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 38755
24.0%
E 38755
24.0%
G 38755
24.0%
C 9418
 
5.8%
W 6486
 
4.0%
D 5828
 
3.6%
I 5828
 
3.6%
V 5828
 
3.6%
O 2932
 
1.8%
N 2932
 
1.8%
Other values (2) 5810
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 161327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 38755
24.0%
E 38755
24.0%
G 38755
24.0%
C 9418
 
5.8%
W 6486
 
4.0%
D 5828
 
3.6%
I 5828
 
3.6%
V 5828
 
3.6%
O 2932
 
1.8%
N 2932
 
1.8%
Other values (2) 5810
 
3.6%

status_description_abbr
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct44
Distinct (%)0.1%
Missing85392
Missing (%)62.7%
Memory size1.0 MiB
A01
40368 
I01
5299 
P01
 
1345
R01
 
1282
W03
 
654
Other values (39)
 
1805

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters152259
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowA01
2nd rowA01
3rd rowA01
4th rowA01
5th rowA01

Common Values

ValueCountFrequency (%)
A01 40368
29.7%
I01 5299
 
3.9%
P01 1345
 
1.0%
R01 1282
 
0.9%
W03 654
 
0.5%
P07 451
 
0.3%
I02 270
 
0.2%
P06 225
 
0.2%
P02 112
 
0.1%
R02 95
 
0.1%
Other values (34) 652
 
0.5%
(Missing) 85392
62.7%

Length

2024-08-08T20:33:00.936087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a01 40368
79.5%
i01 5299
 
10.4%
p01 1345
 
2.7%
r01 1282
 
2.5%
w03 654
 
1.3%
p07 451
 
0.9%
i02 270
 
0.5%
p06 225
 
0.4%
p02 112
 
0.2%
r02 95
 
0.2%
Other values (34) 652
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 50360
33.1%
1 48445
31.8%
A 40368
26.5%
I 5573
 
3.7%
P 2303
 
1.5%
R 1817
 
1.2%
3 781
 
0.5%
2 663
 
0.4%
W 655
 
0.4%
7 471
 
0.3%
Other values (7) 823
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50360
33.1%
1 48445
31.8%
A 40368
26.5%
I 5573
 
3.7%
P 2303
 
1.5%
R 1817
 
1.2%
3 781
 
0.5%
2 663
 
0.4%
W 655
 
0.4%
7 471
 
0.3%
Other values (7) 823
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50360
33.1%
1 48445
31.8%
A 40368
26.5%
I 5573
 
3.7%
P 2303
 
1.5%
R 1817
 
1.2%
3 781
 
0.5%
2 663
 
0.4%
W 655
 
0.4%
7 471
 
0.3%
Other values (7) 823
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50360
33.1%
1 48445
31.8%
A 40368
26.5%
I 5573
 
3.7%
P 2303
 
1.5%
R 1817
 
1.2%
3 781
 
0.5%
2 663
 
0.4%
W 655
 
0.4%
7 471
 
0.3%
Other values (7) 823
 
0.5%

football_name
Text

MISSING 

Distinct3677
Distinct (%)6.5%
Missing79259
Missing (%)58.2%
Memory size1.0 MiB
2024-08-08T20:33:01.163302image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.4086243
Min length2

Characters and Unicode

Total characters307675
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique732 ?
Unique (%)1.3%

Sample

1st rowRabih
2nd rowDonnie
3rd rowTom
4th rowFlozell
5th rowSam
ValueCountFrequency (%)
chris 1003
 
1.8%
mike 771
 
1.4%
brandon 678
 
1.2%
matt 670
 
1.2%
josh 661
 
1.2%
michael 612
 
1.1%
ryan 598
 
1.0%
david 554
 
1.0%
john 520
 
0.9%
jason 511
 
0.9%
Other values (3608) 50398
88.5%
2024-08-08T20:33:01.511631image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 31229
 
10.1%
e 30347
 
9.9%
n 26881
 
8.7%
r 23495
 
7.6%
i 19470
 
6.3%
o 17989
 
5.8%
l 12740
 
4.1%
t 10509
 
3.4%
s 10102
 
3.3%
h 9710
 
3.2%
Other values (47) 115203
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 307675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 31229
 
10.1%
e 30347
 
9.9%
n 26881
 
8.7%
r 23495
 
7.6%
i 19470
 
6.3%
o 17989
 
5.8%
l 12740
 
4.1%
t 10509
 
3.4%
s 10102
 
3.3%
h 9710
 
3.2%
Other values (47) 115203
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 307675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 31229
 
10.1%
e 30347
 
9.9%
n 26881
 
8.7%
r 23495
 
7.6%
i 19470
 
6.3%
o 17989
 
5.8%
l 12740
 
4.1%
t 10509
 
3.4%
s 10102
 
3.3%
h 9710
 
3.2%
Other values (47) 115203
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 307675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 31229
 
10.1%
e 30347
 
9.9%
n 26881
 
8.7%
r 23495
 
7.6%
i 19470
 
6.3%
o 17989
 
5.8%
l 12740
 
4.1%
t 10509
 
3.4%
s 10102
 
3.3%
h 9710
 
3.2%
Other values (47) 115203
37.4%

draft_number
Real number (ℝ)

MISSING 

Distinct269
Distinct (%)0.8%
Missing102533
Missing (%)75.3%
Infinite0
Infinite (%)0.0%
Mean106.86148
Minimum1
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-08-08T20:33:01.627070image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q143
median97
Q3165
95-th percentile235
Maximum329
Range328
Interquartile range (IQR)122

Descriptive statistics

Standard deviation72.572423
Coefficient of variation (CV)0.67912614
Kurtosis-1.0198764
Mean106.86148
Median Absolute Deviation (MAD)59
Skewness0.3505826
Sum3591828
Variance5266.7566
MonotonicityNot monotonic
2024-08-08T20:33:01.750184image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 253
 
0.2%
7 241
 
0.2%
13 231
 
0.2%
6 227
 
0.2%
4 225
 
0.2%
3 219
 
0.2%
9 217
 
0.2%
12 217
 
0.2%
27 216
 
0.2%
8 215
 
0.2%
Other values (259) 31351
 
23.0%
(Missing) 102533
75.3%
ValueCountFrequency (%)
1 253
0.2%
2 206
0.2%
3 219
0.2%
4 225
0.2%
5 215
0.2%
6 227
0.2%
7 241
0.2%
8 215
0.2%
9 217
0.2%
10 214
0.2%
ValueCountFrequency (%)
329 8
< 0.1%
326 6
< 0.1%
320 1
 
< 0.1%
310 1
 
< 0.1%
291 5
< 0.1%
285 4
< 0.1%
276 1
 
< 0.1%
262 4
< 0.1%
261 5
< 0.1%
260 6
< 0.1%

Interactions

2024-08-08T20:32:46.300937image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.481417image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.859899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.224294image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.622262image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.126999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.435232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.734722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.249385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.631700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.924440image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.425876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.752131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.138470image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.629820image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.005098image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.381204image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.570500image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.948982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.318380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.715356image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.214082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.520310image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.821806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.337470image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.714783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.011518image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.507952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.838211image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.222549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.718901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.081102image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.466284image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.661590image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.040086image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.408464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.958584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.294157image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.601383image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.906885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.420552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.793861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.093596image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.588025image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.919294image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.305627image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.804982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.162540image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.555365image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.753678image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.131172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.498552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.050907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.379244image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.686460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.998971image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.511639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.879944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.182685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.678111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.009381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.393711image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.895069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.248618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.633436image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.838862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.217702image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.584633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.137991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.456314image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.764531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.079052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.592712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.956015image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.262763image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.766201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.096463image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.481795image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.985519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.333849image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.715511image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:25.922947image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.300284image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.668717image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.215065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.535389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.843603image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.161139image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.677800image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.034087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.344841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.846274image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.182549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.561876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.068995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.411920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.793581image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.003022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.377354image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.748791image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.291137image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.615468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.918674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.242215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.760879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.110166image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.425921image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.923350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.263628image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.640948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.148898image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.488995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.881661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.092110image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.467436image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.838876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.376224image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.695545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.006754image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.333304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.853973image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.195249image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.519010image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.012432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.354717image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.729031image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.235977image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.572088image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.969743image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.182201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.554515image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.927963image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.461303image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.785641image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.091831image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.425389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.944051image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.279334image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.619105image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.099513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.445803image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.816117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.326066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.657168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.048815image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.263285image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.633587image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.008494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.537374image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.860709image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.169903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.509465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.022121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.352404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.701185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.176592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.531889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.898195image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.404140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.733239image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.131890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.349360image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.719671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.097125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.620453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.944785image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.251979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.737685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.117211image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.442491image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.786262image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.261671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.621976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.983277image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.490218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.816421image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.220971image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.430441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.796875image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.183206image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.708533image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.025859image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.329200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.820767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.200287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.520566image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.869345image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.334737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.711065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.061351image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.571686image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.891490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.310052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.517519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.880959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.271975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.793613image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.112938image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.417409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.912854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.293379image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.609650image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:38.961432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.425823image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.790139image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.155288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.663769image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:45.978568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.393127image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.599607image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:27.963035image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.354041image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.876697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.196013image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.498490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:34.999145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.378462image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.689731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.181643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.505898image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.879226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.236370image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.748847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.061644image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.479205image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.691882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.050121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.445127image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:30.964861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.277087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.578568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.084230image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.466548image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.770292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.264720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.590981image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:41.969315image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.323959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.836943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.145720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:47.557276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:26.765777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:28.129200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:29.523644image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:31.046926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:32.353156image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:33.653644image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:35.166308image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:36.545619image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:37.846363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:39.340797image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:40.664048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:42.048388image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:43.402300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:44.918019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:32:46.217859image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-08T20:33:01.851281image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
depth_chart_positiondraft_clubdraft_numberentry_yearespn_idfantasy_data_idgame_typegsis_it_idheightjersey_numberngs_positionpff_idpositionrookie_yearrotowire_idseasonsleeper_idstatusstatus_description_abbrweekweightyahoo_idyears_exp
depth_chart_position1.0000.0660.1160.1320.0950.1260.0130.1210.2790.4310.7320.0860.9350.1540.1250.3050.1020.0620.0270.0450.1960.0000.119
draft_club0.0661.0000.0710.1640.1610.1870.1490.1670.0860.0600.0590.1240.0810.1800.1590.2500.1450.0310.0330.1030.0680.1070.061
draft_number0.1160.0711.0000.1170.1880.2240.0210.250-0.0690.0020.1110.2320.0930.1170.2350.0460.2380.0450.049-0.037-0.0340.248-0.187
entry_year0.1320.1640.1171.0000.9790.9910.0360.9970.025-0.0590.0960.9770.2301.0000.9960.9140.9910.1290.1720.038-0.0520.994-0.443
espn_id0.0950.1610.1880.9791.0000.9640.0210.970-0.0050.0490.0640.9640.1760.9790.9740.7600.9650.1130.1580.073-0.0400.968-0.379
fantasy_data_id0.1260.1870.2240.9910.9641.0000.0190.993-0.0170.0570.1010.9720.1930.9910.9900.7070.9950.1020.1330.071-0.0420.990-0.373
game_type0.0130.1490.0210.0360.0210.0191.0000.0550.0050.0000.0000.0140.0090.0210.0320.0200.0350.0730.0650.6050.0000.0000.020
gsis_it_id0.1210.1670.2500.9970.9700.9930.0551.000-0.004-0.0130.0900.9720.2100.9970.9950.7970.9950.1130.171-0.045-0.0750.999-0.506
height0.2790.086-0.0690.025-0.005-0.0170.005-0.0041.0000.2210.3850.0010.2600.0250.0050.131-0.0130.0260.0340.0110.742-0.0250.034
jersey_number0.4310.0600.002-0.0590.0490.0570.000-0.0130.2211.0000.6160.0730.512-0.0590.0550.7780.0380.1160.074-0.0020.4150.0340.005
ngs_position0.7320.0590.1110.0960.0640.1010.0000.0900.3850.6161.0000.0570.7650.1180.1531.0000.0681.0000.0000.0000.1610.0000.108
pff_id0.0860.1240.2320.9770.9640.9720.0140.9720.0010.0730.0571.0000.1080.9770.9750.7450.9670.1020.1570.084-0.0290.972-0.373
position0.9350.0810.0930.2300.1760.1930.0090.2100.2600.5120.7650.1081.0000.2890.1820.2510.1570.0370.0950.0990.1550.0600.090
rookie_year0.1540.1800.1171.0000.9790.9910.0210.9970.025-0.0590.1180.9770.2891.0000.9960.9140.9910.1570.1680.038-0.0520.994-0.442
rotowire_id0.1250.1590.2350.9960.9740.9900.0320.9950.0050.0550.1530.9750.1820.9961.0000.7880.9900.1080.1440.048-0.0410.992-0.429
season0.3050.2500.0460.9140.7600.7070.0200.7970.1310.7781.0000.7450.2510.9140.7881.0000.7600.1670.2700.0440.2670.745-0.076
sleeper_id0.1020.1450.2380.9910.9650.9950.0350.995-0.0130.0380.0680.9670.1570.9910.9900.7601.0000.1020.1410.041-0.0560.991-0.441
status0.0620.0310.0450.1290.1130.1020.0730.1130.0260.1161.0000.1020.0370.1570.1080.1670.1021.0000.5450.1750.0300.0000.057
status_description_abbr0.0270.0330.0490.1720.1580.1330.0650.1710.0340.0740.0000.1570.0950.1680.1440.2700.1410.5451.0000.1550.0440.0000.055
week0.0450.103-0.0370.0380.0730.0710.605-0.0450.011-0.0020.0000.0840.0990.0380.0480.0440.0410.1750.1551.0000.0170.0140.028
weight0.1960.068-0.034-0.052-0.040-0.0420.000-0.0750.7420.4150.161-0.0290.155-0.052-0.0410.267-0.0560.0300.0440.0171.000-0.0630.065
yahoo_id0.0000.1070.2480.9940.9680.9900.0000.999-0.0250.0340.0000.9720.0600.9940.9920.7450.9910.0000.0000.014-0.0631.000-0.421
years_exp0.1190.061-0.187-0.443-0.379-0.3730.020-0.5060.0340.0050.108-0.3730.090-0.442-0.429-0.076-0.4410.0570.0550.0280.065-0.4211.000

Missing values

2024-08-08T20:32:47.917609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-08T20:32:48.514191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-08T20:32:49.482075image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seasonteampositiondepth_chart_positionjersey_numberstatusfull_namefirst_namelast_namebirth_dateheightweightcollegegsis_idespn_idsportradar_idyahoo_idrotowire_idpff_idpfr_idfantasy_data_idsleeper_idyears_expheadshot_urlesb_idgsis_it_idsmart_identry_yearrookie_yeardraft_clubngs_positionweekgame_typestatus_description_abbrfootball_namedraft_number
01920AKRDLE0.0ACTScotty BierceBruceBierce1896-09-0369.0164.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/fdvmtlugumgyrxww1uzuNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11920AKRDLE0.0ACTBudge GarrettAlfredGarrett1893-04-1769.0200.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/sdjs03sggqwf0ca8qhzpNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21920AKRDLE0.0ACTGeorge JohnsonGeorgeJohnson1895-07-2273.0205.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/fahqn7dy7n3kvwcrx0doNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31920AKRDLDG0.0ACTAl NesserAlfredNesser1893-06-0672.0195.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/xew88uwps7tcuzhzpn99NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41920AKRDLDG0.0ACTTommy TomlinJohnTomlin1893-09-2570.0197.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/xocsljjtywb3nkms16laNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51920AKROLC0.0ACTRuss BaileyRussellBailey1897-10-1771.0183.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/klfi59kqrwjaaim0ygncNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61920AKROLG0.0ACTGeorge BrownGeorgeBrown1894-08-13NaN190.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bxvgxb33viw7hyirla7wNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71920AKROLG0.0ACTAlf CobbAlfredCobb1893-06-0771.0210.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bigbgkzk5ifmfkhiqpe3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
81920AKROLOT0.0ACTPike JohnsonKarlJohnson1896-05-0271.0185.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/lmqp5iktjgudp9bd4ksfNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
91920AKROLG0.0ACTFrank MoranFrankMoran1891-10-1776.0285.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dhndsd8zkns7ifotqw8lNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
seasonteampositiondepth_chart_positionjersey_numberstatusfull_namefirst_namelast_namebirth_dateheightweightcollegegsis_idespn_idsportradar_idyahoo_idrotowire_idpff_idpfr_idfantasy_data_idsleeper_idyears_expheadshot_urlesb_idgsis_it_idsmart_identry_yearrookie_yeardraft_clubngs_positionweekgame_typestatus_description_abbrfootball_namedraft_number
1361352024MIALBLB50.0RESMohamed KamaraMohamedKamaraNaNNaN250.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57279.0NaN2024.02024.0MIANaN1.0REGR09Mohamed158.0
1361362024CHIWRWR15.0RESRome OdunzeRomeOdunzeNaNNaN215.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57130.0NaN2024.02024.0CHINaN1.0REGR09Rome9.0
1361372024BUFDBDB25.0RESDaequan HardyDaequanHardyNaNNaN178.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57339.0NaN2024.02024.0BUFNaN1.0REGR09Daequan219.0
1361382024CINDLDT90.0RESKris JenkinsKrisJenkinsNaNNaN305.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57170.0NaN2024.02024.0CINNaN1.0REGR09Kris49.0
1361392024JAXDLDT94.0RESMaason SmithMaasonSmithNaNNaN315.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57169.0NaN2024.02024.0JAXNaN1.0REGR09Maason48.0
1361402024DETDBDBNaNRESTerrion ArnoldTerrionArnoldNaNNaN196.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57145.0NaN2024.02024.0DETNaN1.0REGR09Terrion24.0
1361412024PITOLT76.0RESTroy FautanuTroyFautanuNaNNaN317.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57141.0NaN2024.02024.0PITNaN1.0REGR09Troy20.0
1361422024GBOLT77.0RESJordan MorganJordanMorganNaNNaN320.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57146.0NaN2024.02024.0GBNaN1.0REGR09Jordan25.0
1361432024NELBLBNaNE14Jotham RussellJothamRussellNaNNaN235.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57850.0NaN2024.02024.0NaNNaN1.0REGE14JothamNaN
1361442024KCWRWR1.0RESXavier WorthyXavierWorthyNaNNaN172.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN57149.0NaN2024.02024.0KCNaN1.0REGR09Xavier28.0